Image non-uniformity and intensity non-standardness are two major hurdles encountered in human and computer interpretation and analysis of magnetic resonance (MR) images. The former phenomenon is caused by imperfections in the imaging device, specifically the magnetic field, and has been studied extensively, where many solutions have been proposed in the literature. See, for example, Sled JG, et al. “A nonparametric method for automatic correction of intensity non-uniformity in MRI data,” IEEE Trans. Medical Imaging, vol. 17, 87-97 (1998); Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., Gee, J. C., “N4ITK: improved N3 bias correction,” IEEE Transaction on Medical Imaging, 29 (6), 1310-1320 (2010); and Vovk U, Pernus F, Likar B., “A review of methods for correction of intensity inhomogeneity in MRI,” IEEE Trans Med Imaging, 26(3), 405-421 (2007). Generally, the intensity non-uniformity component is assumed to be multiplicative or additive, i.e., the component is multiplicative or additive to an ideal image. Most frequently, the multiplicative model has been used as it is consistent with the inhomogeneous sensitivity of the receiver coil of the magnetic resonance imaging (MRI) scanner. The N3 and N4 algorithms described by Sled et al. and Tustison et al. are two popular correction approaches which have adopted the multiplicative model.
Although to a far lesser extent, solutions have also been presented for the second problem of intensity non-standardness. See, for example, Nyúl LG, Udupa J. K., “On standardizing the MR image intensity scale,” Magn. Reson. Med., 42 (6), 1072-1081 (1999); and Nyúl LG, Udupa J. K., “New variants of a method of MRI scale standardization,” IEEE, Trans. Medical Imaging, vol. 19, 143-150 (2000). This phenomenon causes the lack of a tissue specific numeric intensity meaning, even within the same MRI protocol, for the same body region, for images obtained on the same scanner, and for the same patient. It is also known that all non-uniformity correction methods introduce their own non-standardness as part of the correction process itself (see Madabhushi A, Udupa J. K., “Interplay between intensity standardization and inhomogeneity correction in MR image processing,” IEEE Trans. Medical Imaging, vol. 24, 561-576 (2005)). The interplay between the two processes, proper ways of handling the phenomena in combination, and the manner in which image segmentation performance improves substantially when these problems are addressed properly have also been demonstrated (see, for example, Bai W., Shi W., Ledig C., Rueckert D., “multi-atlas segmentation with augmented features for cardiac MR,” Medical Image Analysis, 19(1), 98-109 (2015); Wu. S., Udupa J. K., Marinaki A., Weinstein S. P., Kontos D., “Intensity standardization in breast MR images improves tissue quantification,” Medical Imaging: Proc. SPIE, vol. 8668, 866822-1-866822-6 (2013); Ge Y, Udupa J. K., et al. “Numerical tissue characterization in MS via standardization of the MR image intensity scale,” Journal of Magn. Reson. Imag, vol. 12, 711-721 (2000); Zhuge Y, Udupa J. K., “Membership-based multiprotocol MR brain image segmentation,” Medical Imaging: Proc. SPIE, vol. 5032, 1572-1579 (2003); and Zhuge, Y., Udupa J. K., et al., “Image background inhomogeneity correction in MRI via intensity standardization,” Comp. Med. Imag. and Graph., 33(1),7-16 (2009)).
However, in many practical situations, automated methods fail to yield acceptable solutions for both problems. This is mainly because solutions for both problems require identifying regions representing the same tissue type for several different tissues, and the automatic strategies, irrespective of the approach, may fail in this task. It remains desirable to provide interactive strategies to overcome this problem in both phenomena wherein the required high level knowledge is provided by an operator to improve image quality substantially in such situations.